Computational cognitive science and artificial intelligence go through periods when different kinds of models are fashionable. Early on, it was all symbolic processing with rules and predicate logic, then people got excited about connectionism, then everyone started putting weights or probabilities on their rules. The current big fad in Cognitive Science is Bayesian modeling. I like this fad, so I'm going to talk about it.

In this post about computational modeling, I promised to talk about how a computational model could improve our understanding of cognition without asserting that people actually did what the computational model did. The basic point is that we can use probabilistic modeling to explore the shape of our data, which in turn constrains what kinds of strategies a human learner could successfully use. In this post, I'll discuss how Bayesian modeling allows us to explore the statistical structure of data and characterize what any (decent) probabilistic algorithm would try to do with that data.